Which method can help reduce the dimensionality of a dataset by preserving significant structures?

Principal Component Analysis (PCA) is a statistical technique specifically designed for dimensionality reduction while preserving the most significant structures within a dataset. It achieves this by transforming the original variables into a new set of variables, known as principal components, which are ordered by the amount of variance they explain in the original data. The first few principal components capture the majority of the dataset's variance, making it possible to reduce the number of dimensions without losing critical information.

This method is particularly useful in scenarios involving large datasets with many features, as it can help simplify models, reduce computational costs, and mitigate issues related to overfitting. The focus on preserving variance ensures that the resulting lower-dimensional representation maintains the essential patterns and relationships within the data, which is crucial for effective analysis and modeling in various applications, particularly in machine learning and AI.

The other options, while they may pertain to data processing, do not primarily aim at reducing dimensionality in a manner that retains significant structural information from the dataset. Thus, they are less effective for achieving the goal outlined in the question.